CmdStan 2.36 no longer requires fixed_param hacks

cmdstanpy/model.py

@@ -205,9 +205,9 @@ def __init__(
                 self._compiler_options.add_include_path(path)
 
             # try to detect models w/out parameters, needed for sampler
-            if not cmdstan_version_before(
-                2, 27
-            ):  # unknown end of version range
+            if not cmdstan_version_before(2, 27) and cmdstan_version_before(
+                2, 36
+            ):
                 try:
                     model_info = self.src_info()
                     if 'parameters' in model_info:

cmdstanpy/stanfit/mcmc.py

@@ -412,7 +412,7 @@ def _assemble_draws(self) -> None:
                     self._step_size[chain] = float(step_size.strip())
                     if self._metadata.cmdstan_config['metric'] != 'unit_e':
                         line = fd.readline().strip()  # metric type
-                        line = fd.readline().lstrip(' #\t')
+                        line = fd.readline().lstrip(' #\t').rstrip()
                         num_unconstrained_params = len(line.split(','))
                         if chain == 0:  # can't allocate w/o num params
                             if self.metric_type == 'diag_e':
@@ -429,18 +429,21 @@ def _assemble_draws(self) -> None:
                                     ),
                                     dtype=float,
                                 )
-                        if self.metric_type == 'diag_e':
-                            xs = line.split(',')
-                            self._metric[chain, :] = [float(x) for x in xs]
-                        else:
-                            xs = line.split(',')
-                            self._metric[chain, 0, :] = [float(x) for x in xs]
-                            for i in range(1, num_unconstrained_params):
-                                line = fd.readline().lstrip(' #\t').strip()
+                        if line:
+                            if self.metric_type == 'diag_e':
                                 xs = line.split(',')
-                                self._metric[chain, i, :] = [
+                                self._metric[chain, :] = [float(x) for x in xs]
+                            else:
+                                xs = line.strip().split(',')
+                                self._metric[chain, 0, :] = [
                                     float(x) for x in xs
                                 ]
+                                for i in range(1, num_unconstrained_params):
+                                    line = fd.readline().lstrip(' #\t').rstrip()
+                                    xs = line.split(',')
+                                    self._metric[chain, i, :] = [
+                                        float(x) for x in xs
+                                    ]
                     else:  # unit_e changed in 2.34 to have an extra line
                         pos = fd.tell()
                         line = fd.readline().strip()

test/test_sample.py

@@ -642,23 +642,23 @@ def test_fixed_param_good() -> None:
     assert datagen_fit.step_size is None
 
 
-def test_fixed_param_unspecified() -> None:
+def test_sample_no_params() -> None:
     stan = os.path.join(DATAFILES_PATH, 'datagen_poisson_glm.stan')
     datagen_model = CmdStanModel(stan_file=stan)
     datagen_fit = datagen_model.sample(iter_sampling=100, show_progress=False)
-    assert datagen_fit.step_size is None
+    assert np.isnan(datagen_fit.step_size).all()
     summary = datagen_fit.summary()
-    assert 'lp__' not in list(summary.index)
+    assert 'lp__' in list(summary.index)
 
     exe_only = os.path.join(DATAFILES_PATH, 'exe_only')
     shutil.copyfile(datagen_model.exe_file, exe_only)
     os.chmod(exe_only, 0o755)
     datagen2_model = CmdStanModel(exe_file=exe_only)
     datagen2_fit = datagen2_model.sample(iter_sampling=200, show_console=True)
     assert datagen2_fit.chains == 4
-    assert datagen2_fit.step_size is None
+    assert np.isnan(datagen2_fit.step_size).all()
     summary = datagen2_fit.summary()
-    assert 'lp__' not in list(summary.index)
+    assert 'lp__' in list(summary.index)
 
 
 def test_index_bounds_error() -> None:
@@ -823,7 +823,7 @@ def test_validate_good_run() -> None:
     assert 'Treedepth satisfactory for all transitions.' in diagnostics
     assert 'No divergent transitions found.' in diagnostics
     assert 'E-BFMI satisfactory' in diagnostics
-    assert 'Effective sample size satisfactory.' in diagnostics
+    assert 'effective sample size satisfactory.' in diagnostics.lower()
 
 
 def test_validate_big_run() -> None:
@@ -1621,33 +1621,18 @@ def test_validate_sample_sig_figs(stanfile='bernoulli.stan'):
 
 
 def test_validate_summary_sig_figs() -> None:
-    # construct CmdStanMCMC from logistic model output, config
-    exe = os.path.join(DATAFILES_PATH, 'logistic' + EXTENSION)
-    rdata = os.path.join(DATAFILES_PATH, 'logistic.data.R')
-    sampler_args = SamplerArgs(iter_sampling=100)
-    cmdstan_args = CmdStanArgs(
-        model_name='logistic',
-        model_exe=exe,
-        chain_ids=[1, 2, 3, 4],
-        seed=12345,
-        data=rdata,
-        output_dir=DATAFILES_PATH,
-        sig_figs=17,
-        method_args=sampler_args,
+    # construct CmdStanMCMC from logistic model output
+    fit = from_csv(
+        [
+            os.path.join(DATAFILES_PATH, 'logistic_output_1.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_2.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_3.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_4.csv'),
+        ]
     )
-    runset = RunSet(args=cmdstan_args, chains=4)
-    runset._csv_files = [
-        os.path.join(DATAFILES_PATH, 'logistic_output_1.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_2.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_3.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_4.csv'),
-    ]
-    retcodes = runset._retcodes
-    for i in range(len(retcodes)):
-        runset._set_retcode(i, 0)
-    fit = CmdStanMCMC(runset)
 
     sum_default = fit.summary()
+
     beta1_default = format(sum_default.iloc[1, 0], '.18g')
     assert beta1_default.startswith('1.3')